Customer grouping for better resources allocation using GA based clustering technique
Appropriate organizational resources allocation becomes a major challenge for companies to address the rapid demands for resources from different operational aspects while resource utilization is keeping low. Differentiate exiting customers with common features into smaller groups can serve as a pie...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/85392 http://hdl.handle.net/10220/11271 |
| _version_ | 1811688520772747264 |
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| author | Ho, G. T. S. Ip, W. H. Lee, C. K. M. Mou, W. L. |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Ho, G. T. S. Ip, W. H. Lee, C. K. M. Mou, W. L. |
| author_sort | Ho, G. T. S. |
| collection | NTU |
| description | Appropriate organizational resources allocation becomes a major challenge for companies to address the rapid demands for resources from different operational aspects while resource utilization is keeping low. Differentiate exiting customers with common features into smaller groups can serve as a piece of useful reference for decision-making. So far, k-means algorithm is the most commonly used clustering technique for conducting customer grouping. However, k-means limits the grouping consideration to a fixed number of dimensions among each group and the grouping results are significantly influenced by the initial clusters means. In this research, a robust genetic algorithm (GA) based k-means clustering algorithm is proposed in attempt to classify existing customers of the enterprise into groups with consideration of relevant attributes for the sake of obtaining desirable grouping results in an efficient manner. Different from k-means, the proposed GA-based k-means algorithm is able to select which and how many dimensions are better to be considered for each customer group when developing approximate optimal solutions. A case study is conducted on a window curtain manufacturer with the application of software Generator associated with MS Excel. |
| first_indexed | 2024-10-01T05:33:31Z |
| format | Journal Article |
| id | ntu-10356/85392 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:33:31Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/853922020-03-07T13:19:23Z Customer grouping for better resources allocation using GA based clustering technique Ho, G. T. S. Ip, W. H. Lee, C. K. M. Mou, W. L. School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering Appropriate organizational resources allocation becomes a major challenge for companies to address the rapid demands for resources from different operational aspects while resource utilization is keeping low. Differentiate exiting customers with common features into smaller groups can serve as a piece of useful reference for decision-making. So far, k-means algorithm is the most commonly used clustering technique for conducting customer grouping. However, k-means limits the grouping consideration to a fixed number of dimensions among each group and the grouping results are significantly influenced by the initial clusters means. In this research, a robust genetic algorithm (GA) based k-means clustering algorithm is proposed in attempt to classify existing customers of the enterprise into groups with consideration of relevant attributes for the sake of obtaining desirable grouping results in an efficient manner. Different from k-means, the proposed GA-based k-means algorithm is able to select which and how many dimensions are better to be considered for each customer group when developing approximate optimal solutions. A case study is conducted on a window curtain manufacturer with the application of software Generator associated with MS Excel. 2013-07-12T02:50:27Z 2019-12-06T16:02:56Z 2013-07-12T02:50:27Z 2019-12-06T16:02:56Z 2011 2011 Journal Article https://hdl.handle.net/10356/85392 http://hdl.handle.net/10220/11271 10.1016/j.eswa.2011.08.045 en Expert systems with applications © 2011 Elsevier Ltd. |
| spellingShingle | DRNTU::Engineering::Mechanical engineering Ho, G. T. S. Ip, W. H. Lee, C. K. M. Mou, W. L. Customer grouping for better resources allocation using GA based clustering technique |
| title | Customer grouping for better resources allocation using GA based clustering technique |
| title_full | Customer grouping for better resources allocation using GA based clustering technique |
| title_fullStr | Customer grouping for better resources allocation using GA based clustering technique |
| title_full_unstemmed | Customer grouping for better resources allocation using GA based clustering technique |
| title_short | Customer grouping for better resources allocation using GA based clustering technique |
| title_sort | customer grouping for better resources allocation using ga based clustering technique |
| topic | DRNTU::Engineering::Mechanical engineering |
| url | https://hdl.handle.net/10356/85392 http://hdl.handle.net/10220/11271 |
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